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Florence Nightingale Colloquium presents Tjeerd van Staa

Tuesday 8 October 2019
The seminar is targeted at a broad audience, in particular we invite PhD candidates and supervisors involved in the Data Science Research programme as well as colleagues from LIACS and MI to attend. The seminar is organized by the DSO, MI and LIACS.
Niels Bohrweg 1
2333 CA Leiden
Tjeerd Van Staa, Professor in Health e-Research, University of Manchester

A Learning Healthcare system: reflections on opportunities and challenges

There is considerable interest in using data collected as part of daily practice in a Learning Healthcare System: data are analysed and results fed back to stakeholders. This presentation will discuss examples and its opportunities and challenges (based on analyses of large primary care datasets). One is around antibiotic prescribing care in primary care. While there is considerable focus on reducing overall levels of antibiotic prescribing, research has found that higher levels of prescribing are associated with better outcomes (such as reduced rates of hospital admission). Is this policy sensible? Another example is around cardiovascular risk prediction for primary prevention. Routinely collected data are widely used in the UK to predict CVD risk and target treatment; it has been proposed that machine-learning models could substantially improve risk prediction. Does this hype stand up to reality?

Tjeerd van Staa studied medicine and received his degree in 1987 at the Erasmus University of Rotterdam, the Netherlands. After several years of working as a practising physician, he joined the pharmaceutical industry and obtained a MSc in Epidemiology as well as in Medical Law and Ethics. 
In May 2014, he became Professor of Health eResearch at the Centre of Health Informatics of Manchester University. One of his current research interests is Efficient Trials, which aims to harness advanced health informatics and electronic health records to improve clinical trials. Another interest is the Learning HealthCare System in which routinely collected data are used to feedback actionable information to clinicians and patients. One research project is around optimising antibiotic prescribing in primary care. Recent work has focused on measuring the uncertainty and lack of generalisibility of risk prediction models that use routinely collected data (such as electronic health records from primary care). He has published over 260 peer-reviewed articles and is a well-recognised speaker in the field of pragmatic trials and clinical epidemiology.

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